Dimensionality reduction (DR) refers to the problem of projecting high-dimensional data onto a low-dimensional manifold so that relevant information is preserved. DR arises in many application areas where direct processing of the data is too costly. Through a machine-learning perspective that focuses on algorithms rather than theory, Dimensionality Reduction provides an overview of methods for DR including real-world applications taken from areas such as speech processing and computer vision. Interest in this area has exploded in recent years, making it a growing field of research. This book serves as the first reference for interested graduate students and researchers.
- ISBN: 978-1-58488-653-2
- Editorial: Chapman & Hall/CRC Statistics and Mathem
- Encuadernacion: Cartoné
- Páginas: 320
- Fecha Publicación: 15/02/2010
- Nº Volúmenes: 1
- Idioma: Inglés